1. Bipolar Connectivity 2. Predicting Antidepressant Response
VOL 2 #7
Resting state functional connectivity in women with bipolar disorder during clinical remission.
Syan SK(1,)(2), Minuzzi L(1,)(2,)(3,)(4), Smith M(4), Allega OR(1,)(2), Hall GB(1,)(5), Frey BN(1,)(2,)(3,)(4).
Bipolar Disord. 2017 Mar;19(2):97-106. doi: 10.1111/bdi.12469. Epub 2017 Mar 4.
(1)MiNDS Neuroscience Graduate Program, McMaster University
(2)Women’s Health Concerns Clinic, St. Joseph’s Healthcare
(3)Mood Disorders Program, St. Joseph’s Healthcare
(4)Department of Psychiatry and Behavioural Neurosciences, McMaster University,
(5)Department of Psychology, Neuroscience and Behaviour,
McMaster University, Hamilton, ON, Canada.
Periods of euthymia in bipolar disorder (BD) serve as a valuable time to study trait-based pathophysiology. The use of resting state functional connectivity (Rs-FC) can aid in the understanding of BD pathophysiology free of task or mood state biases. The present study investigated two unexplored areas of Rs-FC research in bipolar remission: (i) Rs-FC in women, controlling for the potential influence of premenstrual symptoms, and (ii) the use of both independent component analysis (ICA) and seed-based analysis (SBA) to investigate Rs-FC.
The authors investigated Rs-FC of the default mode network, meso-paralimbic network and fronto-parietal network in a sample of 32 euthymic women with BD and 36 age-matched controls during the mid-follicular phase of their menstrual cycle.
Rs-FC was assessed with ICA and SBA using the posterior cingulate cortex (PCC), amygdala and dorsolateral prefrontal cortex (dlPFC) as seed points for their respective resting state networks.
In BD, compared to controls, SBAs revealed increased coupling between the PCC and the angular gyrus (P=.002, false discovery rate [FDR]-corrected) and between the right dlPFC and the brainstem (P=.03, FDR-corrected). In BD only, PCC-angular gyrus coupling was correlated with anxiety symptoms.
Group differences in Rs-FC using ICA did not survive multiple comparisons.
Negative findings from whole-brain ICA Rs-FC may reflect a state of clinical remission in BD. Heightened activation between the PCC and the angular gyrus and between the dlPFC and the brainstem may reflect (i) an abnormal trait integration of affective information during clinical remission and/or (ii) an adaptive compensatory mechanism required for clinical stabilization.
Reevaluating the Efficacy and Predictability of Antidepressant TreatmentsA Symptom Clustering Approach
JAMA Psychiatry. 2017;74(4):370-378. doi:10.1001/jamapsychiatry.2017.0025
Are antidepressants equally good at treating different kinds of symptoms in depression?
Individual patient data from 9 clinical trials of major depression in 7221 patients were analyzed, with a focus on specific clusters of symptoms rather than total depressive severity. For each cluster, significant differences in efficacy between antidepressants were identified.
Antidepressant medications can be selected to benefit specific clusters of symptoms in depression.
Depressive severity is typically measured according to total scores on questionnaires that include a diverse range of symptoms despite convincing evidence that depression is not a unitary construct. When evaluated according to aggregate measurements, treatment efficacy is generally modest and differences in efficacy between antidepressant therapies are small.
To determine the efficacy of antidepressant treatments on empirically defined groups of symptoms and examine the replicability of these groups.
Design, Setting, and Participants
Patient-reported data on patients with depression from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial (n = 4039) were used to identify clusters of symptoms in a depressive symptom checklist.]
The findings were then replicated using the Combining Medications to Enhance Depression Outcomes (CO-MED) trial (n = 640).
Mixed-effects regression analysis was then performed to determine whether observed symptom clusters have differential response trajectories using intent-to-treat data from both trials (n = 4706) along with 7 additional placebo and active-comparator phase 3 trials of duloxetine (n = 2515).
Finally, outcomes for each cluster were estimated separately using machine-learning approaches. The study was conducted from October 28, 2014, to May 19, 2016.
Main Outcomes and Measures
Twelve items from the self-reported Quick Inventory of Depressive Symptomatology (QIDS-SR) scale and 14 items from the clinician-rated Hamilton Depression (HAM-D) rating scale. Higher scores on the measures indicate greater severity of the symptoms.
Of the 4706 patients included in the first analysis, 1722 (36.6%) were male; mean (SD) age was 41.2 (13.3) years. Of the 2515 patients included in the second analysis, 855 (34.0%) were male; mean age was 42.65 (12.17) years.
Three symptom clusters in the QIDS-SR scale were identified at baseline in STAR*D. This 3-cluster solution was replicated in CO-MED and was similar for the HAM-D scale.
Antidepressants in general (8 of 9 treatments) were more effective for core emotional symptoms than for sleep or atypical symptoms. Differences in efficacy between drugs were often greater than the difference in efficacy between treatments and placebo. For example, high-dose duloxetine outperformed escitalopram in treating core emotional symptoms (effect size, 2.3 HAM-D points during 8 weeks, 95% CI, 1.6 to 3.1; P < .001), but escitalopram was not significantly different from placebo (effect size, 0.03 HAM-D points; 95% CI, −0.7 to 0.8; P = .94).
Conclusions and Relevance
Two common checklists used to measure depressive severity can produce statistically reliable clusters of symptoms. These clusters differ in their responsiveness to treatment both within and across different antidepressant medications. Selecting the best drug for a given cluster may have a bigger benefit than that gained by use of an active compound vs a placebo.